PIXELBANKv8.2.1
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Confusion Matrix

EasyModel Evaluation

Build a confusion matrix for binary classification results.

Given lists of true labels and predicted labels (each 0 or 1), compute the 2x2 confusion matrix:

[TNFPFNTP]\begin{bmatrix} TN & FP \\ FN & TP \end{bmatrix}

where:

  • TP (True Positive): predicted 1, actual 1
  • TN (True Negative): predicted 0, actual 0
  • FP (False Positive): predicted 1, actual 0
  • FN (False Negative): predicted 0, actual 1

Return the matrix as a 2D list [[TN, FP], [FN, TP]].

Example:

Input:
y_true = [1, 0, 1, 1, 0, 0]
y_pred = [1, 0, 0, 1, 0, 1]
Output:
[[2, 1], [1, 2]]
Reasoning:
  • We iterate over the y_true and y_pred lists simultaneously, comparing each pair of true and predicted labels.
  • For each pair, we check the conditions for TP, TN, FP, and FN and increment the corresponding counter:
    • TP if ytrue=1y_{true} = 1 and ypred=1y_{pred} = 1,
    • TN if ytrue=0y_{true} = 0 and ypred=0y_{pred} = 0,
    • FP if ytrue=0y_{true} = 0 and ypred=1y_{pred} = 1,
    • FN if ytrue=1y_{true} = 1 and ypred=0y_{pred} = 0.
  • After iterating over all pairs, we count:
    • TN: 2 (for the pairs (0,0) at indices 1 and 4),
    • FP: 1 (for the pair (0,1) at index 5),
    • FN: 1 (for the pair (1,0) at index 2),
    • TP: 2 (for the pairs (1,1) at indices 0 and 3).
  • The final output is the 2x2 confusion matrix: [[TN,FP],[FN,TP]]=[[2,1],[1,2]][[TN, FP], [FN, TP]] = [[2, 1], [1, 2]].

Constraints:

  • y_true and y_pred are lists of 0s and 1s of equal length
  • Return a 2D list [[TN, FP], [FN, TP]]
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